Introduction

Power analysis is the name given to the process for determining the sample size for a
research study. The technical definition of power is that it is the probability of
detecting a “true” effect when it exists. Many students think that there is a simple
formula for determining sample size for every research situation. However, the reality
it that there are many research situations that are so complex that they almost defy
rational power analysis. In most cases, power analysis involves a number of
simplifying assumptions, in order to make the problem tractable, and running the
analyses numerous times with different variations to cover all of the contingencies.

In this unit we will try to illustrate how to do a power analysis for a test of
two independent proportions, i.e., the response variable has two levels and the
predictor variable also has two levels. Instead of analyzing these data using a test
of independent proportions, we could compute a chi-square statistic in a 2×2 contingency
table or run a simple logistic regression analysis. These three analyses yield the same
results and would require the same sample sizes to test effects.

Description of the Experiment

It is known that a certain type of skin lesion will develop into cancer in 30% of
patients if left untreated. There is a drug on the market that will reduce the probability
of cancer developing by 10%. A pharmaceutical company is developing a new drug to treat
skin lesions but it will only be worthwhile to do so if the new drug reduces the
probability of developing skin cancer by at least 15%, an additional 5% beyond
the existing drug.

The pharmaceutical company plans to do a study with patients randomly assigned to
two groups, the control (untreated) group and the treatment group. The company wants to
know how many subjects will be needed to test a difference in proportions of .15 with a power
of .8 at alpha equal to .05.

The Power Analysis

We will make use of the SAS proc power to determine the
sample size needed for tests of two independent proportions as well as for tests of means.
The proc power needs the following information in order to do
the power analysis: 1) the expected proportion of cancer the untreated group (p1
= .3), 2) the expected proportion of cancer in the treated group (p2 = .3 – .15 = .15), 3)
the alpha level (alpha = .05, the default for proc power), and 4) the required level of power
(power = .8 for this experiment). There are still different versions of the same
test, Pearson’s chi-square test, the likelihood ratio test and Fisher’s exact
test. Let’s run all three of them and see how different they are from each
other.

Pearson’s chi-square test is the most commonly used. Likelihood ratio test
was developed after the Pearson’s chi-square test and is also very common. Both
of the methods are based on the asymptotic theory and work well when sample size
is large. When sample size is small Fisher’s exact method is usually more
conservative.

This is all well and good but a two-sided test doesn’t make much sense in this situation.
We want to test for a drug that reduces the probability of cancer not for one that increases
the probability. In this case we should be using one-tail test and we do this by
using the sides=1 option in proc power. We are going to use
Fisher’s exact test from now on.

Now because we believe that we know a lot about the incidence of cancer in the untreated
group we would like to make the control group half as large as the treatment group. We can
use the groupweights option to achieve this. Notice that this option only
works with ntotal =.

As you can see, by the way we have specified the ratio, we need 1/3 of the
total sample for group 1 and 2/3 for group 2. This turns out to be 80 and 160
for the two groups. So we see that we will need more subjects overall than for equal sized groups but
we can have a much smaller untreated group.

In the end, the company has decided to use 75 patients in the control group
and 150 in the treatment group. Let’s see what the power is.